3. Detailed Data Inspection (Prepare Phase)
3.1 Import check
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Import Check ---\n\n")
}
--- Import Check ---
head(anxiety_data_raw)
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:06 CST"
3.2 Column Name Cleaning
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Column Name Cleaning ---\n\n")
}
--- Column Name Cleaning ---
anxiety_data_clean_names <- janitor::clean_names(anxiety_data_raw)
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:06 CST"
3.3 Data Structure
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Data Structure ---\n\n")
}
--- Data Structure ---
str(anxiety_data_clean_names)
spc_tbl_ [12,000 × 20] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ id : num [1:12000] 1 2 3 4 5 6 7 8 9 10 ...
$ age : num [1:12000] 56 46 32 60 25 38 56 36 40 28 ...
$ gender : chr [1:12000] "Female" "Male" "Female" "Male" ...
$ occupation : chr [1:12000] "Other" "Teacher" "Doctor" "Doctor" ...
$ sleep_hours : num [1:12000] 9.6 6.4 6.9 9.2 9.2 6.7 3.8 4.9 6.2 8.3 ...
$ physical_activity_hrs_week : num [1:12000] 8.3 7.3 1 3.7 2.5 9.9 7.5 0.5 9 9.3 ...
$ caffeine_intake_mg_day : num [1:12000] 175 97 467 471 364 194 411 413 284 148 ...
$ alcohol_consumption_drinks_week: num [1:12000] 6 6 14 16 2 16 13 4 6 18 ...
$ smoking : chr [1:12000] "No" "No" "No" "No" ...
$ family_history_of_anxiety : chr [1:12000] "No" "No" "No" "Yes" ...
$ stress_level_1_10 : num [1:12000] 4 3 2 6 7 2 2 3 4 5 ...
$ heart_rate_bpm_during_attack : num [1:12000] 145 143 60 94 152 174 81 88 121 145 ...
$ breathing_rate_breaths_min : num [1:12000] 33 18 34 19 15 25 22 36 28 12 ...
$ sweating_level_1_5 : num [1:12000] 3 5 1 1 4 3 4 5 2 4 ...
$ dizziness : chr [1:12000] "No" "Yes" "No" "No" ...
$ medication : chr [1:12000] "No" "No" "No" "Yes" ...
$ therapy_sessions_per_month : num [1:12000] 4 0 7 4 0 2 5 6 0 3 ...
$ recent_major_life_event : chr [1:12000] "Yes" "No" "Yes" "Yes" ...
$ diet_quality_1_10 : num [1:12000] 9 9 10 5 1 1 10 4 5 10 ...
$ severity_of_anxiety_attack_1_10: num [1:12000] 10 8 5 8 1 8 10 2 4 4 ...
- attr(*, "spec")=
.. cols(
.. ID = col_double(),
.. Age = col_double(),
.. Gender = col_character(),
.. Occupation = col_character(),
.. `Sleep Hours` = col_double(),
.. `Physical Activity (hrs/week)` = col_double(),
.. `Caffeine Intake (mg/day)` = col_double(),
.. `Alcohol Consumption (drinks/week)` = col_double(),
.. Smoking = col_character(),
.. `Family History of Anxiety` = col_character(),
.. `Stress Level (1-10)` = col_double(),
.. `Heart Rate (bpm during attack)` = col_double(),
.. `Breathing Rate (breaths/min)` = col_double(),
.. `Sweating Level (1-5)` = col_double(),
.. Dizziness = col_character(),
.. Medication = col_character(),
.. `Therapy Sessions (per month)` = col_double(),
.. `Recent Major Life Event` = col_character(),
.. `Diet Quality (1-10)` = col_double(),
.. `Severity of Anxiety Attack (1-10)` = col_double()
.. )
- attr(*, "problems")=<externalptr>
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:06 CST"
3.4 Data Distribution
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Data Summary ---\n\n")
}
--- Data Summary ---
summary(anxiety_data_clean_names)
id age gender
Min. : 1 Min. :18.00 Length:12000
1st Qu.: 3001 1st Qu.:29.00 Class :character
Median : 6000 Median :41.00 Mode :character
Mean : 6000 Mean :40.97
3rd Qu.: 9000 3rd Qu.:53.00
Max. :12000 Max. :64.00
occupation sleep_hours
Length:12000 Min. : 3.000
Class :character 1st Qu.: 4.800
Mode :character Median : 6.500
Mean : 6.483
3rd Qu.: 8.200
Max. :10.000
physical_activity_hrs_week caffeine_intake_mg_day
Min. : 0.000 Min. : 0.0
1st Qu.: 2.500 1st Qu.:122.0
Median : 5.000 Median :244.0
Mean : 5.031 Mean :246.7
3rd Qu.: 7.525 3rd Qu.:371.0
Max. :10.000 Max. :499.0
alcohol_consumption_drinks_week smoking
Min. : 0.000 Length:12000
1st Qu.: 5.000 Class :character
Median : 9.000 Mode :character
Mean : 9.493
3rd Qu.:15.000
Max. :19.000
family_history_of_anxiety stress_level_1_10
Length:12000 Min. : 1.000
Class :character 1st Qu.: 3.000
Mode :character Median : 5.000
Mean : 5.462
3rd Qu.: 8.000
Max. :10.000
heart_rate_bpm_during_attack breathing_rate_breaths_min
Min. : 60.0 Min. :12.00
1st Qu.: 89.0 1st Qu.:18.00
Median :119.0 Median :25.00
Mean :119.4 Mean :25.46
3rd Qu.:149.0 3rd Qu.:32.00
Max. :179.0 Max. :39.00
sweating_level_1_5 dizziness medication
Min. :1.000 Length:12000 Length:12000
1st Qu.:2.000 Class :character Class :character
Median :3.000 Mode :character Mode :character
Mean :2.987
3rd Qu.:4.000
Max. :5.000
therapy_sessions_per_month recent_major_life_event
Min. :0.000 Length:12000
1st Qu.:2.000 Class :character
Median :5.000 Mode :character
Mean :4.518
3rd Qu.:7.000
Max. :9.000
diet_quality_1_10 severity_of_anxiety_attack_1_10
Min. : 1.000 Min. : 1.000
1st Qu.: 3.000 1st Qu.: 3.000
Median : 5.000 Median : 6.000
Mean : 5.497 Mean : 5.508
3rd Qu.: 8.000 3rd Qu.: 8.000
Max. :10.000 Max. :10.000
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:07 CST"
3.5 Further Details
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Detailed Data Summary (skimr) ---\n\n")
}
--- Detailed Data Summary (skimr) ---
skim(anxiety_data_clean_names)
── Data Summary ────────────────────────
Values
Name anxiety_data_clean_names
Number of rows 12000
Number of columns 20
_______________________
Column type frequency:
character 7
numeric 13
________________________
Group variables None
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:08 CST"
4. Detailed Variable Examination (Prepare Phase Visualizations)
4.1 Categorical Variable Analysis
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Categorical Variable Plots ---\n\n")
}
--- Categorical Variable Plots ---
categorical_vars <- c("gender", "occupation", "smoking", "family_history_of_anxiety",
"dizziness", "medication", "recent_major_life_event")
# Generate plots and tables, storing them in a list
categorical_results <- lapply(categorical_vars, function(var) {
plot_categorical(anxiety_data_clean_names, var)
})
[1] "Frequency Table for gender :"
Female Male Other
5809 5723 468
[1] "Frequency Table for occupation :"
Doctor Engineer Other Student Teacher
2004 1953 1971 1953 1980
Unemployed
2139
[1] "Frequency Table for smoking :"
No Yes
8417 3583
[1] "Frequency Table for family_history_of_anxiety :"
No Yes
7179 4821
[1] "Frequency Table for dizziness :"
No Yes
8406 3594
[1] "Frequency Table for medication :"
No Yes
9605 2395
[1] "Frequency Table for recent_major_life_event :"
No Yes
9054 2946






# Extract just the plots for combining
categorical_plots <- lapply(categorical_results, function(x) x$plot)
# Combine and save
combine_and_save(categorical_plots, file.path(plots_folder, "combined_categorical_plots.png"), type = "categorical")

if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:11 CST"
4.2 Numeric Variable Analysis
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Numeric Variable Plots ---\n\n")
}
--- Numeric Variable Plots ---
numeric_vars <- c("age", "sleep_hours", "physical_activity_hrs_week",
"caffeine_intake_mg_day", "alcohol_consumption_drinks_week",
"stress_level_1_10", "heart_rate_bpm_during_attack",
"breathing_rate_breaths_min", "sweating_level_1_5",
"therapy_sessions_per_month", "diet_quality_1_10",
"severity_of_anxiety_attack_1_10")
# Generate plots, storing them in a list
numeric_results <- lapply(numeric_vars, function(var) {
plot_numeric(anxiety_data_clean_names, var)
})
[1] "Histogram for age"
[1] "Boxplot for age"
[1] "Histogram for sleep_hours"
[1] "Boxplot for sleep_hours"
[1] "Histogram for physical_activity_hrs_week"
[1] "Boxplot for physical_activity_hrs_week"
[1] "Histogram for caffeine_intake_mg_day"
[1] "Boxplot for caffeine_intake_mg_day"
[1] "Histogram for alcohol_consumption_drinks_week"
[1] "Boxplot for alcohol_consumption_drinks_week"
[1] "Histogram for stress_level_1_10"
[1] "Boxplot for stress_level_1_10"
[1] "Histogram for heart_rate_bpm_during_attack"
[1] "Boxplot for heart_rate_bpm_during_attack"
[1] "Histogram for breathing_rate_breaths_min"
[1] "Boxplot for breathing_rate_breaths_min"
[1] "Histogram for sweating_level_1_5"
[1] "Boxplot for sweating_level_1_5"
[1] "Histogram for therapy_sessions_per_month"
[1] "Boxplot for therapy_sessions_per_month"
[1] "Histogram for diet_quality_1_10"
[1] "Boxplot for diet_quality_1_10"
[1] "Histogram for severity_of_anxiety_attack_1_10"
[1] "Boxplot for severity_of_anxiety_attack_1_10"























# Combine and save numeric plots (histograms and boxplots)
combine_and_save(numeric_results, file.path(plots_folder, "combined_numeric_plots.png"), type = "numeric")

if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:17 CST"
4.3 Data Type Conversion Plan
The following variables will be converted to factors in the Process
phase:
- gender: Categorical variable.
- occupation: Categorical variable.
- smoking: Categorical (Yes/No).
- family_history_of_anxiety: Categorical
(Yes/No).
- dizziness: Categorical (Yes/No).
- medication: Categorical (Yes/No).
- recent_major_life_event: Categorical (Yes/No).
4.4 Duplicate Check
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Duplicate Check ---\n\n")
}
--- Duplicate Check ---
duplicates<- anxiety_data_clean_names %>%
duplicated() %>%
sum()
print("Number of Duplicate Rows:")
[1] "Number of Duplicate Rows:"
print(duplicates)
[1] 0
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:17 CST"
4.5 Explicit Missing Value Check
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Explicit Missing Value Check ---\n\n")
}
--- Explicit Missing Value Check ---
missing_values <- colSums(is.na(anxiety_data_clean_names))
print("Missing Values per Column:")
[1] "Missing Values per Column:"
print(missing_values)
id
0
age
0
gender
0
occupation
0
sleep_hours
0
physical_activity_hrs_week
0
caffeine_intake_mg_day
0
alcohol_consumption_drinks_week
0
smoking
0
family_history_of_anxiety
0
stress_level_1_10
0
heart_rate_bpm_during_attack
0
breathing_rate_breaths_min
0
sweating_level_1_5
0
dizziness
0
medication
0
therapy_sessions_per_month
0
recent_major_life_event
0
diet_quality_1_10
0
severity_of_anxiety_attack_1_10
0
missing_percentages <- colMeans(is.na(anxiety_data_clean_names)) * 100
print("Percentage of Missing Values per Column:")
[1] "Percentage of Missing Values per Column:"
print(missing_percentages)
id
0
age
0
gender
0
occupation
0
sleep_hours
0
physical_activity_hrs_week
0
caffeine_intake_mg_day
0
alcohol_consumption_drinks_week
0
smoking
0
family_history_of_anxiety
0
stress_level_1_10
0
heart_rate_bpm_during_attack
0
breathing_rate_breaths_min
0
sweating_level_1_5
0
dizziness
0
medication
0
therapy_sessions_per_month
0
recent_major_life_event
0
diet_quality_1_10
0
severity_of_anxiety_attack_1_10
0
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:17 CST"
5. Data Processing (Process Phase)
This section details the data cleaning and transformation steps,
addressing the issues and plans identified in the Prepare phase.
5.1 Data Type Conversion
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Data Type Conversion ---\n\n")
}
--- Data Type Conversion ---
# Create a copy for processing
anxiety_data_processed <- anxiety_data_clean_names
# Convert character variables to factors
categorical_vars <- c("gender", "occupation", "smoking", "family_history_of_anxiety",
"dizziness", "medication", "recent_major_life_event")
anxiety_data_processed <- anxiety_data_processed %>%
mutate(across(all_of(categorical_vars), as.factor))
# Verify conversion
str(anxiety_data_processed)
tibble [12,000 × 20] (S3: tbl_df/tbl/data.frame)
$ id : num [1:12000] 1 2 3 4 5 6 7 8 9 10 ...
$ age : num [1:12000] 56 46 32 60 25 38 56 36 40 28 ...
$ gender : Factor w/ 3 levels "Female","Male",..: 1 2 1 2 2 2 2 2 2 1 ...
$ occupation : Factor w/ 6 levels "Doctor","Engineer",..: 3 5 1 1 4 4 1 5 1 1 ...
$ sleep_hours : num [1:12000] 9.6 6.4 6.9 9.2 9.2 6.7 3.8 4.9 6.2 8.3 ...
$ physical_activity_hrs_week : num [1:12000] 8.3 7.3 1 3.7 2.5 9.9 7.5 0.5 9 9.3 ...
$ caffeine_intake_mg_day : num [1:12000] 175 97 467 471 364 194 411 413 284 148 ...
$ alcohol_consumption_drinks_week: num [1:12000] 6 6 14 16 2 16 13 4 6 18 ...
$ smoking : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 1 1 1 ...
$ family_history_of_anxiety : Factor w/ 2 levels "No","Yes": 1 1 1 2 2 2 2 1 1 2 ...
$ stress_level_1_10 : num [1:12000] 4 3 2 6 7 2 2 3 4 5 ...
$ heart_rate_bpm_during_attack : num [1:12000] 145 143 60 94 152 174 81 88 121 145 ...
$ breathing_rate_breaths_min : num [1:12000] 33 18 34 19 15 25 22 36 28 12 ...
$ sweating_level_1_5 : num [1:12000] 3 5 1 1 4 3 4 5 2 4 ...
$ dizziness : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 1 1 1 ...
$ medication : Factor w/ 2 levels "No","Yes": 1 1 1 2 2 2 1 2 1 1 ...
$ therapy_sessions_per_month : num [1:12000] 4 0 7 4 0 2 5 6 0 3 ...
$ recent_major_life_event : Factor w/ 2 levels "No","Yes": 2 1 2 2 1 2 2 1 1 1 ...
$ diet_quality_1_10 : num [1:12000] 9 9 10 5 1 1 10 4 5 10 ...
$ severity_of_anxiety_attack_1_10: num [1:12000] 10 8 5 8 1 8 10 2 4 4 ...
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:17 CST"
5.2 Outlier Investigation and Handling
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Outlier Investigation and Handling ---\n\n")
}
--- Outlier Investigation and Handling ---
# --- sleep_hours ---
# Investigate values < 4
low_sleep <- anxiety_data_processed %>% filter(sleep_hours < 4)
print("Observations with sleep_hours < 4:")
[1] "Observations with sleep_hours < 4:"
print(low_sleep)
# Decision: Keep. While low, these values are plausible.
# --- physical_activity_hrs_week ---
# Investigate values > 9
high_activity <- anxiety_data_processed %>% filter(physical_activity_hrs_week > 9)
print("Observations with physical_activity_hrs_week > 9:")
[1] "Observations with physical_activity_hrs_week > 9:"
print(high_activity)
# Decision: Keep. These are high but plausible values.
# --- caffeine_intake_mg_day ---
# Investigate values > 400
high_caffeine <- anxiety_data_processed %>% filter(caffeine_intake_mg_day > 400)
print("Observations with caffeine_intake_mg_day > 400:")
[1] "Observations with caffeine_intake_mg_day > 400:"
print(high_caffeine)
# Decision: Keep. These are high, but plausible, values.
# --- alcohol_consumption_drinks_week ---
# Investigate values > 14
high_alcohol <- anxiety_data_processed %>% filter(alcohol_consumption_drinks_week > 14)
print("Observations with alcohol_consumption_drinks_week > 14:")
[1] "Observations with alcohol_consumption_drinks_week > 14:"
print(high_alcohol)
# Decision: Keep. While above recommended limits, they are plausible.
# --- heart_rate_bpm_during_attack ---
# Investigate values < 70 and > 160
low_hr <- anxiety_data_processed %>% filter(heart_rate_bpm_during_attack < 70)
print("Observations with heart_rate_bpm_during_attack < 70:")
[1] "Observations with heart_rate_bpm_during_attack < 70:"
print(low_hr)
high_hr <- anxiety_data_processed %>% filter(heart_rate_bpm_during_attack > 160)
print("Observations with heart_rate_bpm_during_attack > 160:")
[1] "Observations with heart_rate_bpm_during_attack > 160:"
print(high_hr)
# Decision: Keep. After reviewing the context, values are kept.
# --- breathing_rate_breaths_min ---
# Investigate values < 15 and > 35
low_br <- anxiety_data_processed %>% filter(breathing_rate_breaths_min < 15)
print("Observations with breathing_rate_breaths_min < 15:")
[1] "Observations with breathing_rate_breaths_min < 15:"
print(low_br)
high_br <- anxiety_data_processed %>% filter(breathing_rate_breaths_min > 35)
print("Observations with breathing_rate_breaths_min > 35:")
[1] "Observations with breathing_rate_breaths_min > 35:"
print(high_br)
# Decision: Keep. After reviewing the context, values are kept.
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:18 CST"
5.3. Variable Creation
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Variable Creation ---\n\n")
}
--- Variable Creation ---
# --- High Stress Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
mutate(high_stress = ifelse(stress_level_1_10 >= 8, 1, 0))
# --- High Severity Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
mutate(high_severity = ifelse(severity_of_anxiety_attack_1_10 >= 8, 1, 0))
# --- Untreated Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
mutate(untreated = ifelse(high_stress == 1 & high_severity == 1 & therapy_sessions_per_month == 0 & medication == "No", 1, 0))
# --- Low Sleep Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
mutate(low_sleep = ifelse(sleep_hours < 7, 1, 0))
# --- High Alcohol Consumption Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
mutate(high_alcohol = ifelse( (gender == "Female" & alcohol_consumption_drinks_week >= 8) |
(gender == "Male" & alcohol_consumption_drinks_week >= 15) |
(gender == "Other" & alcohol_consumption_drinks_week >= 15)
, 1, 0))
# --- High Caffeine Consumption Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
mutate(high_caffeine = ifelse(caffeine_intake_mg_day > 400, 1, 0))
#Verify
str(anxiety_data_processed)
tibble [12,000 × 26] (S3: tbl_df/tbl/data.frame)
$ id : num [1:12000] 1 2 3 4 5 6 7 8 9 10 ...
$ age : num [1:12000] 56 46 32 60 25 38 56 36 40 28 ...
$ gender : Factor w/ 3 levels "Female","Male",..: 1 2 1 2 2 2 2 2 2 1 ...
$ occupation : Factor w/ 6 levels "Doctor","Engineer",..: 3 5 1 1 4 4 1 5 1 1 ...
$ sleep_hours : num [1:12000] 9.6 6.4 6.9 9.2 9.2 6.7 3.8 4.9 6.2 8.3 ...
$ physical_activity_hrs_week : num [1:12000] 8.3 7.3 1 3.7 2.5 9.9 7.5 0.5 9 9.3 ...
$ caffeine_intake_mg_day : num [1:12000] 175 97 467 471 364 194 411 413 284 148 ...
$ alcohol_consumption_drinks_week: num [1:12000] 6 6 14 16 2 16 13 4 6 18 ...
$ smoking : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 1 1 1 ...
$ family_history_of_anxiety : Factor w/ 2 levels "No","Yes": 1 1 1 2 2 2 2 1 1 2 ...
$ stress_level_1_10 : num [1:12000] 4 3 2 6 7 2 2 3 4 5 ...
$ heart_rate_bpm_during_attack : num [1:12000] 145 143 60 94 152 174 81 88 121 145 ...
$ breathing_rate_breaths_min : num [1:12000] 33 18 34 19 15 25 22 36 28 12 ...
$ sweating_level_1_5 : num [1:12000] 3 5 1 1 4 3 4 5 2 4 ...
$ dizziness : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 1 1 1 ...
$ medication : Factor w/ 2 levels "No","Yes": 1 1 1 2 2 2 1 2 1 1 ...
$ therapy_sessions_per_month : num [1:12000] 4 0 7 4 0 2 5 6 0 3 ...
$ recent_major_life_event : Factor w/ 2 levels "No","Yes": 2 1 2 2 1 2 2 1 1 1 ...
$ diet_quality_1_10 : num [1:12000] 9 9 10 5 1 1 10 4 5 10 ...
$ severity_of_anxiety_attack_1_10: num [1:12000] 10 8 5 8 1 8 10 2 4 4 ...
$ high_stress : num [1:12000] 0 0 0 0 0 0 0 0 0 0 ...
$ high_severity : num [1:12000] 1 1 0 1 0 1 1 0 0 0 ...
$ untreated : num [1:12000] 0 0 0 0 0 0 0 0 0 0 ...
$ low_sleep : num [1:12000] 0 1 1 0 0 1 1 1 1 0 ...
$ high_alcohol : num [1:12000] 0 0 1 1 0 1 0 0 0 1 ...
$ high_caffeine : num [1:12000] 0 0 1 1 0 0 1 1 0 0 ...
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:19 CST"
5.4. Verification
# --- Verification ---
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Verification ---\n\n")
}
--- Verification ---
# Check for NA's again in the new variables
missing_values_processed <- colSums(is.na(anxiety_data_processed))
print("Missing Values per Column After Processing:")
[1] "Missing Values per Column After Processing:"
print(missing_values_processed)
id
0
age
0
gender
0
occupation
0
sleep_hours
0
physical_activity_hrs_week
0
caffeine_intake_mg_day
0
alcohol_consumption_drinks_week
0
smoking
0
family_history_of_anxiety
0
stress_level_1_10
0
heart_rate_bpm_during_attack
0
breathing_rate_breaths_min
0
sweating_level_1_5
0
dizziness
0
medication
0
therapy_sessions_per_month
0
recent_major_life_event
0
diet_quality_1_10
0
severity_of_anxiety_attack_1_10
0
high_stress
0
high_severity
0
untreated
0
low_sleep
0
high_alcohol
0
high_caffeine
0
# Check for Duplicates again
duplicates_processed <- anxiety_data_processed %>%
duplicated() %>%
sum()
print("Number of Duplicate Rows After Processing:")
[1] "Number of Duplicate Rows After Processing:"
print(duplicates_processed)
[1] 0
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:19 CST"
6. Data Analysis (Analyze Phase)
6.1. Descriptive Statistics (Targeted Groups)
# --- Descriptive Statistics (Targeted Groups) ---
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Descriptive Statistics (Targeted Groups) ---\n\n")
}
--- Descriptive Statistics (Targeted Groups) ---
library(knitr) # Make sure knitr is loaded
# --- Overall Descriptive Statistics ---
cat("\nOverall Descriptive Statistics:\n")
Overall Descriptive Statistics:
print(kable(skim(anxiety_data_processed), format = "markdown"))
| factor |
gender |
0 |
1 |
FALSE |
3 |
Fem: 5809, Mal: 5723, Oth: 468 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
occupation |
0 |
1 |
FALSE |
6 |
Une: 2139, Doc: 2004, Tea: 1980, Oth: 1971 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
smoking |
0 |
1 |
FALSE |
2 |
No: 8417, Yes: 3583 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
family_history_of_anxiety |
0 |
1 |
FALSE |
2 |
No: 7179, Yes: 4821 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
dizziness |
0 |
1 |
FALSE |
2 |
No: 8406, Yes: 3594 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
medication |
0 |
1 |
FALSE |
2 |
No: 9605, Yes: 2395 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
recent_major_life_event |
0 |
1 |
FALSE |
2 |
No: 9054, Yes: 2946 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| numeric |
id |
0 |
1 |
NA |
NA |
NA |
6000.5000000 |
3464.2459497 |
1 |
3000.75 |
6000.5 |
9000.250 |
12000 |
▇▇▇▇▇ |
| numeric |
age |
0 |
1 |
NA |
NA |
NA |
40.9667500 |
13.4732799 |
18 |
29.00 |
41.0 |
53.000 |
64 |
▇▇▇▇▇ |
| numeric |
sleep_hours |
0 |
1 |
NA |
NA |
NA |
6.4826500 |
2.0148852 |
3 |
4.80 |
6.5 |
8.200 |
10 |
▇▇▇▇▇ |
| numeric |
physical_activity_hrs_week |
0 |
1 |
NA |
NA |
NA |
5.0308917 |
2.8890002 |
0 |
2.50 |
5.0 |
7.525 |
10 |
▇▇▇▇▇ |
| numeric |
caffeine_intake_mg_day |
0 |
1 |
NA |
NA |
NA |
246.6960833 |
144.4870713 |
0 |
122.00 |
244.0 |
371.000 |
499 |
▇▇▇▇▇ |
| numeric |
alcohol_consumption_drinks_week |
0 |
1 |
NA |
NA |
NA |
9.4928333 |
5.7693635 |
0 |
5.00 |
9.0 |
15.000 |
19 |
▇▇▇▇▇ |
| numeric |
stress_level_1_10 |
0 |
1 |
NA |
NA |
NA |
5.4622500 |
2.8972011 |
1 |
3.00 |
5.0 |
8.000 |
10 |
▇▇▇▇▇ |
| numeric |
heart_rate_bpm_during_attack |
0 |
1 |
NA |
NA |
NA |
119.3985000 |
34.8067114 |
60 |
89.00 |
119.0 |
149.000 |
179 |
▇▇▇▇▇ |
| numeric |
breathing_rate_breaths_min |
0 |
1 |
NA |
NA |
NA |
25.4623333 |
8.0906862 |
12 |
18.00 |
25.0 |
32.000 |
39 |
▇▆▇▆▇ |
| numeric |
sweating_level_1_5 |
0 |
1 |
NA |
NA |
NA |
2.9874167 |
1.4144817 |
1 |
2.00 |
3.0 |
4.000 |
5 |
▇▇▇▇▇ |
| numeric |
therapy_sessions_per_month |
0 |
1 |
NA |
NA |
NA |
4.5184167 |
2.8660098 |
0 |
2.00 |
5.0 |
7.000 |
9 |
▇▇▇▇▇ |
| numeric |
diet_quality_1_10 |
0 |
1 |
NA |
NA |
NA |
5.4973333 |
2.8675794 |
1 |
3.00 |
5.0 |
8.000 |
10 |
▇▇▇▇▇ |
| numeric |
severity_of_anxiety_attack_1_10 |
0 |
1 |
NA |
NA |
NA |
5.5075833 |
2.8586635 |
1 |
3.00 |
6.0 |
8.000 |
10 |
▇▇▇▇▇ |
| numeric |
high_stress |
0 |
1 |
NA |
NA |
NA |
0.2951667 |
0.4561367 |
0 |
0.00 |
0.0 |
1.000 |
1 |
▇▁▁▁▃ |
| numeric |
high_severity |
0 |
1 |
NA |
NA |
NA |
0.2970833 |
0.4569926 |
0 |
0.00 |
0.0 |
1.000 |
1 |
▇▁▁▁▃ |
| numeric |
untreated |
0 |
1 |
NA |
NA |
NA |
0.0057500 |
0.0756136 |
0 |
0.00 |
0.0 |
0.000 |
1 |
▇▁▁▁▁ |
| numeric |
low_sleep |
0 |
1 |
NA |
NA |
NA |
0.5736667 |
0.4945641 |
0 |
0.00 |
1.0 |
1.000 |
1 |
▆▁▁▁▇ |
| numeric |
high_alcohol |
0 |
1 |
NA |
NA |
NA |
0.4219167 |
0.4938859 |
0 |
0.00 |
0.0 |
1.000 |
1 |
▇▁▁▁▆ |
| numeric |
high_caffeine |
0 |
1 |
NA |
NA |
NA |
0.1913333 |
0.3933672 |
0 |
0.00 |
0.0 |
0.000 |
1 |
▇▁▁▁▂ |
# --- High Stress Group (stress_level_1_10 >= 8) ---
cat("\nHigh Stress Group (stress_level_1_10 >= 8):\n")
High Stress Group (stress_level_1_10 >= 8):
high_stress_skim <- anxiety_data_processed %>%
filter(high_stress == 1) %>%
skim()
print(kable(high_stress_skim, format = "markdown"))
| factor |
gender |
0 |
1 |
FALSE |
3 |
Fem: 1714, Mal: 1684, Oth: 144 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
occupation |
0 |
1 |
FALSE |
6 |
Une: 643, Doc: 618, Oth: 593, Tea: 583 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
smoking |
0 |
1 |
FALSE |
2 |
No: 2498, Yes: 1044 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
family_history_of_anxiety |
0 |
1 |
FALSE |
2 |
No: 2167, Yes: 1375 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
dizziness |
0 |
1 |
FALSE |
2 |
No: 2510, Yes: 1032 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
medication |
0 |
1 |
FALSE |
2 |
No: 2845, Yes: 697 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
recent_major_life_event |
0 |
1 |
FALSE |
2 |
No: 2653, Yes: 889 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| numeric |
id |
0 |
1 |
NA |
NA |
NA |
5983.1894410 |
3465.0240856 |
21 |
2960.25 |
6028.0 |
8894.75 |
11999 |
▇▇▇▇▇ |
| numeric |
age |
0 |
1 |
NA |
NA |
NA |
41.4212309 |
13.4551408 |
18 |
30.00 |
42.0 |
53.00 |
64 |
▇▇▇▇▇ |
| numeric |
sleep_hours |
0 |
1 |
NA |
NA |
NA |
6.4819029 |
2.0236801 |
3 |
4.70 |
6.5 |
8.20 |
10 |
▇▇▇▇▇ |
| numeric |
physical_activity_hrs_week |
0 |
1 |
NA |
NA |
NA |
5.0207228 |
2.8872153 |
0 |
2.60 |
5.0 |
7.50 |
10 |
▇▇▇▇▇ |
| numeric |
caffeine_intake_mg_day |
0 |
1 |
NA |
NA |
NA |
250.9618859 |
145.5021705 |
0 |
126.00 |
249.0 |
376.00 |
499 |
▇▇▇▇▇ |
| numeric |
alcohol_consumption_drinks_week |
0 |
1 |
NA |
NA |
NA |
9.5118577 |
5.7096509 |
0 |
5.00 |
9.0 |
14.00 |
19 |
▇▇▇▇▇ |
| numeric |
stress_level_1_10 |
0 |
1 |
NA |
NA |
NA |
9.0271033 |
0.8145455 |
8 |
8.00 |
9.0 |
10.00 |
10 |
▇▁▇▁▇ |
| numeric |
heart_rate_bpm_during_attack |
0 |
1 |
NA |
NA |
NA |
118.8740824 |
34.6482622 |
60 |
89.00 |
119.0 |
150.00 |
179 |
▇▇▇▇▇ |
| numeric |
breathing_rate_breaths_min |
0 |
1 |
NA |
NA |
NA |
25.4271598 |
8.0480888 |
12 |
19.00 |
25.0 |
32.00 |
39 |
▇▇▇▆▇ |
| numeric |
sweating_level_1_5 |
0 |
1 |
NA |
NA |
NA |
2.9664032 |
1.4169070 |
1 |
2.00 |
3.0 |
4.00 |
5 |
▇▇▇▇▇ |
| numeric |
therapy_sessions_per_month |
0 |
1 |
NA |
NA |
NA |
4.5982496 |
2.8629285 |
0 |
2.00 |
5.0 |
7.00 |
9 |
▇▇▇▇▇ |
| numeric |
diet_quality_1_10 |
0 |
1 |
NA |
NA |
NA |
5.5143986 |
2.8628030 |
1 |
3.00 |
6.0 |
8.00 |
10 |
▇▇▇▇▇ |
| numeric |
severity_of_anxiety_attack_1_10 |
0 |
1 |
NA |
NA |
NA |
5.5203275 |
2.8677937 |
1 |
3.00 |
5.0 |
8.00 |
10 |
▇▇▇▇▇ |
| numeric |
high_stress |
0 |
1 |
NA |
NA |
NA |
1.0000000 |
0.0000000 |
1 |
1.00 |
1.0 |
1.00 |
1 |
▁▁▇▁▁ |
| numeric |
high_severity |
0 |
1 |
NA |
NA |
NA |
0.3043478 |
0.4601956 |
0 |
0.00 |
0.0 |
1.00 |
1 |
▇▁▁▁▃ |
| numeric |
untreated |
0 |
1 |
NA |
NA |
NA |
0.0194805 |
0.1382260 |
0 |
0.00 |
0.0 |
0.00 |
1 |
▇▁▁▁▁ |
| numeric |
low_sleep |
0 |
1 |
NA |
NA |
NA |
0.5705816 |
0.4950631 |
0 |
0.00 |
1.0 |
1.00 |
1 |
▆▁▁▁▇ |
| numeric |
high_alcohol |
0 |
1 |
NA |
NA |
NA |
0.4223602 |
0.4940050 |
0 |
0.00 |
0.0 |
1.00 |
1 |
▇▁▁▁▆ |
| numeric |
high_caffeine |
0 |
1 |
NA |
NA |
NA |
0.2027103 |
0.4020752 |
0 |
0.00 |
0.0 |
0.00 |
1 |
▇▁▁▁▂ |
# --- High Severity Group (severity_of_anxiety_attack_1_10 >= 8) ---
cat("\nHigh Severity Group (severity_of_anxiety_attack_1_10 >= 8):\n")
High Severity Group (severity_of_anxiety_attack_1_10 >= 8):
high_severity_skim <- anxiety_data_processed %>%
filter(high_severity == 1) %>%
skim()
print(kable(high_severity_skim, format = "markdown"))
| factor |
gender |
0 |
1 |
FALSE |
3 |
Fem: 1720, Mal: 1707, Oth: 138 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
occupation |
0 |
1 |
FALSE |
6 |
Une: 650, Oth: 595, Tea: 587, Eng: 584 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
smoking |
0 |
1 |
FALSE |
2 |
No: 2530, Yes: 1035 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
family_history_of_anxiety |
0 |
1 |
FALSE |
2 |
No: 2105, Yes: 1460 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
dizziness |
0 |
1 |
FALSE |
2 |
No: 2448, Yes: 1117 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
medication |
0 |
1 |
FALSE |
2 |
No: 2862, Yes: 703 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
recent_major_life_event |
0 |
1 |
FALSE |
2 |
No: 2711, Yes: 854 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| numeric |
id |
0 |
1 |
NA |
NA |
NA |
5941.5506311 |
3468.7468716 |
1 |
2921.0 |
5874.0 |
8941.0 |
11998 |
▇▇▇▇▇ |
| numeric |
age |
0 |
1 |
NA |
NA |
NA |
40.7542777 |
13.5025743 |
18 |
29.0 |
41.0 |
52.0 |
64 |
▇▇▇▇▇ |
| numeric |
sleep_hours |
0 |
1 |
NA |
NA |
NA |
6.4638710 |
2.0430583 |
3 |
4.7 |
6.4 |
8.2 |
10 |
▇▇▇▇▇ |
| numeric |
physical_activity_hrs_week |
0 |
1 |
NA |
NA |
NA |
5.1044320 |
2.8752890 |
0 |
2.6 |
5.1 |
7.7 |
10 |
▇▇▇▇▇ |
| numeric |
caffeine_intake_mg_day |
0 |
1 |
NA |
NA |
NA |
247.4659187 |
144.4413647 |
0 |
122.0 |
246.0 |
371.0 |
499 |
▇▇▇▇▇ |
| numeric |
alcohol_consumption_drinks_week |
0 |
1 |
NA |
NA |
NA |
9.4737728 |
5.7484883 |
0 |
5.0 |
9.0 |
14.0 |
19 |
▇▇▇▇▇ |
| numeric |
stress_level_1_10 |
0 |
1 |
NA |
NA |
NA |
5.4995792 |
2.9142847 |
1 |
3.0 |
6.0 |
8.0 |
10 |
▇▇▇▇▇ |
| numeric |
heart_rate_bpm_during_attack |
0 |
1 |
NA |
NA |
NA |
119.2244039 |
35.1393429 |
60 |
88.0 |
119.0 |
150.0 |
179 |
▇▇▇▇▇ |
| numeric |
breathing_rate_breaths_min |
0 |
1 |
NA |
NA |
NA |
25.5481066 |
8.1092256 |
12 |
19.0 |
26.0 |
33.0 |
39 |
▇▆▇▆▇ |
| numeric |
sweating_level_1_5 |
0 |
1 |
NA |
NA |
NA |
3.0067321 |
1.4138007 |
1 |
2.0 |
3.0 |
4.0 |
5 |
▇▇▇▇▇ |
| numeric |
therapy_sessions_per_month |
0 |
1 |
NA |
NA |
NA |
4.4796634 |
2.8818765 |
0 |
2.0 |
4.0 |
7.0 |
9 |
▇▇▇▇▇ |
| numeric |
diet_quality_1_10 |
0 |
1 |
NA |
NA |
NA |
5.4712482 |
2.8523477 |
1 |
3.0 |
5.0 |
8.0 |
10 |
▇▇▇▇▇ |
| numeric |
severity_of_anxiety_attack_1_10 |
0 |
1 |
NA |
NA |
NA |
9.0000000 |
0.8161529 |
8 |
8.0 |
9.0 |
10.0 |
10 |
▇▁▇▁▇ |
| numeric |
high_stress |
0 |
1 |
NA |
NA |
NA |
0.3023843 |
0.4593552 |
0 |
0.0 |
0.0 |
1.0 |
1 |
▇▁▁▁▃ |
| numeric |
high_severity |
0 |
1 |
NA |
NA |
NA |
1.0000000 |
0.0000000 |
1 |
1.0 |
1.0 |
1.0 |
1 |
▁▁▇▁▁ |
| numeric |
untreated |
0 |
1 |
NA |
NA |
NA |
0.0193548 |
0.1377881 |
0 |
0.0 |
0.0 |
0.0 |
1 |
▇▁▁▁▁ |
| numeric |
low_sleep |
0 |
1 |
NA |
NA |
NA |
0.5733520 |
0.4946596 |
0 |
0.0 |
1.0 |
1.0 |
1 |
▆▁▁▁▇ |
| numeric |
high_alcohol |
0 |
1 |
NA |
NA |
NA |
0.4185133 |
0.4933844 |
0 |
0.0 |
0.0 |
1.0 |
1 |
▇▁▁▁▆ |
| numeric |
high_caffeine |
0 |
1 |
NA |
NA |
NA |
0.1921459 |
0.3940424 |
0 |
0.0 |
0.0 |
0.0 |
1 |
▇▁▁▁▂ |
# --- Untreated Group (high_stress, high_severity, no therapy/medication) ---
cat("\nUntreated Group (High Stress, High Severity, No Therapy/Medication):\n")
Untreated Group (High Stress, High Severity, No Therapy/Medication):
untreated_skim <- anxiety_data_processed %>%
filter(untreated == 1) %>%
skim()
print(kable(untreated_skim, format = "markdown"))
| factor |
gender |
0 |
1 |
FALSE |
3 |
Mal: 37, Fem: 31, Oth: 1 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
occupation |
0 |
1 |
FALSE |
6 |
Doc: 14, Eng: 13, Oth: 13, Une: 12 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
smoking |
0 |
1 |
FALSE |
2 |
No: 47, Yes: 22 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
family_history_of_anxiety |
0 |
1 |
FALSE |
2 |
No: 39, Yes: 30 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
dizziness |
0 |
1 |
FALSE |
2 |
No: 47, Yes: 22 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
medication |
0 |
1 |
FALSE |
1 |
No: 69, Yes: 0 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
recent_major_life_event |
0 |
1 |
FALSE |
2 |
No: 53, Yes: 16 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| numeric |
id |
0 |
1 |
NA |
NA |
NA |
5219.0289855 |
3847.4373479 |
63.0 |
1707.0 |
4542.0 |
9409.0 |
11872 |
▇▅▃▃▆ |
| numeric |
age |
0 |
1 |
NA |
NA |
NA |
39.6666667 |
12.6568589 |
18.0 |
30.0 |
40.0 |
50.0 |
64 |
▇▇▇▇▅ |
| numeric |
sleep_hours |
0 |
1 |
NA |
NA |
NA |
6.5478261 |
2.1423330 |
3.0 |
4.8 |
6.7 |
8.4 |
10 |
▇▆▅▇▇ |
| numeric |
physical_activity_hrs_week |
0 |
1 |
NA |
NA |
NA |
5.5652174 |
2.9555942 |
0.3 |
2.7 |
5.9 |
8.3 |
10 |
▅▅▇▆▇ |
| numeric |
caffeine_intake_mg_day |
0 |
1 |
NA |
NA |
NA |
257.5362319 |
149.7427775 |
1.0 |
149.0 |
254.0 |
377.0 |
495 |
▆▆▆▆▇ |
| numeric |
alcohol_consumption_drinks_week |
0 |
1 |
NA |
NA |
NA |
9.8985507 |
5.3030095 |
0.0 |
5.0 |
10.0 |
14.0 |
19 |
▃▆▇▃▇ |
| numeric |
stress_level_1_10 |
0 |
1 |
NA |
NA |
NA |
8.8985507 |
0.8427010 |
8.0 |
8.0 |
9.0 |
10.0 |
10 |
▇▁▆▁▆ |
| numeric |
heart_rate_bpm_during_attack |
0 |
1 |
NA |
NA |
NA |
109.1014493 |
33.2057512 |
61.0 |
79.0 |
107.0 |
135.0 |
175 |
▇▅▇▆▂ |
| numeric |
breathing_rate_breaths_min |
0 |
1 |
NA |
NA |
NA |
27.2463768 |
8.1842852 |
12.0 |
21.0 |
29.0 |
34.0 |
39 |
▅▃▅▆▇ |
| numeric |
sweating_level_1_5 |
0 |
1 |
NA |
NA |
NA |
3.0144928 |
1.4500786 |
1.0 |
2.0 |
3.0 |
4.0 |
5 |
▇▆▇▆▇ |
| numeric |
therapy_sessions_per_month |
0 |
1 |
NA |
NA |
NA |
0.0000000 |
0.0000000 |
0.0 |
0.0 |
0.0 |
0.0 |
0 |
▁▁▇▁▁ |
| numeric |
diet_quality_1_10 |
0 |
1 |
NA |
NA |
NA |
5.2028986 |
2.9234333 |
1.0 |
2.0 |
5.0 |
7.0 |
10 |
▇▅▇▆▅ |
| numeric |
severity_of_anxiety_attack_1_10 |
0 |
1 |
NA |
NA |
NA |
9.0000000 |
0.8401681 |
8.0 |
8.0 |
9.0 |
10.0 |
10 |
▇▁▇▁▇ |
| numeric |
high_stress |
0 |
1 |
NA |
NA |
NA |
1.0000000 |
0.0000000 |
1.0 |
1.0 |
1.0 |
1.0 |
1 |
▁▁▇▁▁ |
| numeric |
high_severity |
0 |
1 |
NA |
NA |
NA |
1.0000000 |
0.0000000 |
1.0 |
1.0 |
1.0 |
1.0 |
1 |
▁▁▇▁▁ |
| numeric |
untreated |
0 |
1 |
NA |
NA |
NA |
1.0000000 |
0.0000000 |
1.0 |
1.0 |
1.0 |
1.0 |
1 |
▁▁▇▁▁ |
| numeric |
low_sleep |
0 |
1 |
NA |
NA |
NA |
0.5362319 |
0.5023389 |
0.0 |
0.0 |
1.0 |
1.0 |
1 |
▇▁▁▁▇ |
| numeric |
high_alcohol |
0 |
1 |
NA |
NA |
NA |
0.4347826 |
0.4993602 |
0.0 |
0.0 |
0.0 |
1.0 |
1 |
▇▁▁▁▆ |
| numeric |
high_caffeine |
0 |
1 |
NA |
NA |
NA |
0.2463768 |
0.4340574 |
0.0 |
0.0 |
0.0 |
0.0 |
1 |
▇▁▁▁▂ |
# --- Low Sleep Group (sleep_hours < 7) ---
cat("\nLow Sleep Group (sleep_hours < 7):\n")
Low Sleep Group (sleep_hours < 7):
low_sleep_skim <- anxiety_data_processed %>%
filter(low_sleep == 1) %>%
skim()
print(kable(low_sleep_skim, format = "markdown"))
| factor |
gender |
0 |
1 |
FALSE |
3 |
Fem: 3324, Mal: 3281, Oth: 279 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
occupation |
0 |
1 |
FALSE |
6 |
Une: 1236, Doc: 1155, Stu: 1143, Oth: 1135 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
smoking |
0 |
1 |
FALSE |
2 |
No: 4894, Yes: 1990 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
family_history_of_anxiety |
0 |
1 |
FALSE |
2 |
No: 4095, Yes: 2789 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
dizziness |
0 |
1 |
FALSE |
2 |
No: 4805, Yes: 2079 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
medication |
0 |
1 |
FALSE |
2 |
No: 5515, Yes: 1369 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
recent_major_life_event |
0 |
1 |
FALSE |
2 |
No: 5194, Yes: 1690 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| numeric |
id |
0 |
1 |
NA |
NA |
NA |
6029.9456711 |
3450.6706995 |
2 |
3058.75 |
6044.5 |
9008.50 |
12000.0 |
▇▇▇▇▇ |
| numeric |
age |
0 |
1 |
NA |
NA |
NA |
40.8782684 |
13.3999377 |
18 |
29.00 |
41.0 |
52.00 |
64.0 |
▇▇▇▇▇ |
| numeric |
sleep_hours |
0 |
1 |
NA |
NA |
NA |
4.9948576 |
1.1452000 |
3 |
4.00 |
5.0 |
6.00 |
6.9 |
▇▇▇▇▇ |
| numeric |
physical_activity_hrs_week |
0 |
1 |
NA |
NA |
NA |
5.0198141 |
2.8583361 |
0 |
2.60 |
5.0 |
7.50 |
10.0 |
▇▇▇▇▇ |
| numeric |
caffeine_intake_mg_day |
0 |
1 |
NA |
NA |
NA |
246.2486926 |
144.6147327 |
0 |
120.00 |
244.0 |
372.00 |
499.0 |
▇▇▇▇▇ |
| numeric |
alcohol_consumption_drinks_week |
0 |
1 |
NA |
NA |
NA |
9.3999128 |
5.7901965 |
0 |
4.00 |
9.0 |
14.25 |
19.0 |
▇▇▇▇▇ |
| numeric |
stress_level_1_10 |
0 |
1 |
NA |
NA |
NA |
5.4402963 |
2.9002613 |
1 |
3.00 |
5.0 |
8.00 |
10.0 |
▇▇▇▇▇ |
| numeric |
heart_rate_bpm_during_attack |
0 |
1 |
NA |
NA |
NA |
119.9251888 |
34.6850391 |
60 |
90.00 |
120.0 |
150.00 |
179.0 |
▇▇▇▇▇ |
| numeric |
breathing_rate_breaths_min |
0 |
1 |
NA |
NA |
NA |
25.5261476 |
8.1236118 |
12 |
19.00 |
25.0 |
33.00 |
39.0 |
▇▇▇▆▇ |
| numeric |
sweating_level_1_5 |
0 |
1 |
NA |
NA |
NA |
2.9818420 |
1.4096207 |
1 |
2.00 |
3.0 |
4.00 |
5.0 |
▇▇▇▇▇ |
| numeric |
therapy_sessions_per_month |
0 |
1 |
NA |
NA |
NA |
4.5302150 |
2.8511607 |
0 |
2.00 |
5.0 |
7.00 |
9.0 |
▇▇▇▇▇ |
| numeric |
diet_quality_1_10 |
0 |
1 |
NA |
NA |
NA |
5.4841662 |
2.8586557 |
1 |
3.00 |
5.0 |
8.00 |
10.0 |
▇▇▇▇▇ |
| numeric |
severity_of_anxiety_attack_1_10 |
0 |
1 |
NA |
NA |
NA |
5.5017432 |
2.8589023 |
1 |
3.00 |
5.0 |
8.00 |
10.0 |
▇▇▇▇▇ |
| numeric |
high_stress |
0 |
1 |
NA |
NA |
NA |
0.2935793 |
0.4554346 |
0 |
0.00 |
0.0 |
1.00 |
1.0 |
▇▁▁▁▃ |
| numeric |
high_severity |
0 |
1 |
NA |
NA |
NA |
0.2969204 |
0.4569344 |
0 |
0.00 |
0.0 |
1.00 |
1.0 |
▇▁▁▁▃ |
| numeric |
untreated |
0 |
1 |
NA |
NA |
NA |
0.0053748 |
0.0731209 |
0 |
0.00 |
0.0 |
0.00 |
1.0 |
▇▁▁▁▁ |
| numeric |
low_sleep |
0 |
1 |
NA |
NA |
NA |
1.0000000 |
0.0000000 |
1 |
1.00 |
1.0 |
1.00 |
1.0 |
▁▁▇▁▁ |
| numeric |
high_alcohol |
0 |
1 |
NA |
NA |
NA |
0.4171993 |
0.4931322 |
0 |
0.00 |
0.0 |
1.00 |
1.0 |
▇▁▁▁▆ |
| numeric |
high_caffeine |
0 |
1 |
NA |
NA |
NA |
0.1911679 |
0.3932496 |
0 |
0.00 |
0.0 |
0.00 |
1.0 |
▇▁▁▁▂ |
# --- High Alcohol Group ---
cat("\nHigh Alcohol Group:\n")
High Alcohol Group:
high_alcohol_skim <- anxiety_data_processed %>%
filter(high_alcohol == 1) %>%
skim()
print(kable(high_alcohol_skim, format = "markdown"))
| factor |
gender |
0 |
1 |
FALSE |
3 |
Fem: 3501, Mal: 1439, Oth: 123 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
occupation |
0 |
1 |
FALSE |
6 |
Une: 868, Oth: 864, Stu: 854, Doc: 845 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
smoking |
0 |
1 |
FALSE |
2 |
No: 3553, Yes: 1510 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
family_history_of_anxiety |
0 |
1 |
FALSE |
2 |
No: 3042, Yes: 2021 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
dizziness |
0 |
1 |
FALSE |
2 |
No: 3584, Yes: 1479 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
medication |
0 |
1 |
FALSE |
2 |
No: 4036, Yes: 1027 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
recent_major_life_event |
0 |
1 |
FALSE |
2 |
No: 3785, Yes: 1278 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| numeric |
id |
0 |
1 |
NA |
NA |
NA |
6067.8498914 |
3458.2837367 |
3 |
3034.0 |
6196.0 |
9031.5 |
11998 |
▇▇▇▇▇ |
| numeric |
age |
0 |
1 |
NA |
NA |
NA |
40.9691882 |
13.4769158 |
18 |
30.0 |
41.0 |
52.0 |
64 |
▇▇▇▇▇ |
| numeric |
sleep_hours |
0 |
1 |
NA |
NA |
NA |
6.5171440 |
2.0199407 |
3 |
4.8 |
6.5 |
8.3 |
10 |
▇▇▇▇▇ |
| numeric |
physical_activity_hrs_week |
0 |
1 |
NA |
NA |
NA |
5.0041872 |
2.8816286 |
0 |
2.5 |
5.0 |
7.5 |
10 |
▇▇▇▇▇ |
| numeric |
caffeine_intake_mg_day |
0 |
1 |
NA |
NA |
NA |
244.5083942 |
143.8814713 |
0 |
120.5 |
239.0 |
367.0 |
499 |
▇▇▇▇▇ |
| numeric |
alcohol_consumption_drinks_week |
0 |
1 |
NA |
NA |
NA |
14.5475015 |
3.4412816 |
8 |
12.0 |
15.0 |
17.0 |
19 |
▅▂▂▅▇ |
| numeric |
stress_level_1_10 |
0 |
1 |
NA |
NA |
NA |
5.4799526 |
2.9014402 |
1 |
3.0 |
5.0 |
8.0 |
10 |
▇▇▇▇▇ |
| numeric |
heart_rate_bpm_during_attack |
0 |
1 |
NA |
NA |
NA |
118.6662058 |
34.7234144 |
60 |
88.0 |
118.0 |
149.0 |
179 |
▇▇▇▇▇ |
| numeric |
breathing_rate_breaths_min |
0 |
1 |
NA |
NA |
NA |
25.4270196 |
8.1594974 |
12 |
18.0 |
26.0 |
33.0 |
39 |
▇▆▇▆▇ |
| numeric |
sweating_level_1_5 |
0 |
1 |
NA |
NA |
NA |
2.9812364 |
1.4141589 |
1 |
2.0 |
3.0 |
4.0 |
5 |
▇▇▇▇▇ |
| numeric |
therapy_sessions_per_month |
0 |
1 |
NA |
NA |
NA |
4.5018764 |
2.8786786 |
0 |
2.0 |
4.0 |
7.0 |
9 |
▇▇▇▇▇ |
| numeric |
diet_quality_1_10 |
0 |
1 |
NA |
NA |
NA |
5.5328856 |
2.8585885 |
1 |
3.0 |
6.0 |
8.0 |
10 |
▇▇▇▇▇ |
| numeric |
severity_of_anxiety_attack_1_10 |
0 |
1 |
NA |
NA |
NA |
5.4959510 |
2.8408902 |
1 |
3.0 |
5.0 |
8.0 |
10 |
▇▇▇▇▇ |
| numeric |
high_stress |
0 |
1 |
NA |
NA |
NA |
0.2954770 |
0.4563019 |
0 |
0.0 |
0.0 |
1.0 |
1 |
▇▁▁▁▃ |
| numeric |
high_severity |
0 |
1 |
NA |
NA |
NA |
0.2946869 |
0.4559469 |
0 |
0.0 |
0.0 |
1.0 |
1 |
▇▁▁▁▃ |
| numeric |
untreated |
0 |
1 |
NA |
NA |
NA |
0.0059253 |
0.0767554 |
0 |
0.0 |
0.0 |
0.0 |
1 |
▇▁▁▁▁ |
| numeric |
low_sleep |
0 |
1 |
NA |
NA |
NA |
0.5672526 |
0.4955054 |
0 |
0.0 |
1.0 |
1.0 |
1 |
▆▁▁▁▇ |
| numeric |
high_alcohol |
0 |
1 |
NA |
NA |
NA |
1.0000000 |
0.0000000 |
1 |
1.0 |
1.0 |
1.0 |
1 |
▁▁▇▁▁ |
| numeric |
high_caffeine |
0 |
1 |
NA |
NA |
NA |
0.1860557 |
0.3891900 |
0 |
0.0 |
0.0 |
0.0 |
1 |
▇▁▁▁▂ |
# --- High Caffeine Group ---
cat("\nHigh Caffeine Group:\n")
High Caffeine Group:
high_caffeine_skim <- anxiety_data_processed %>%
filter(high_caffeine == 1) %>%
skim()
print(kable(high_caffeine_skim, format = "markdown"))
| factor |
gender |
0 |
1 |
FALSE |
3 |
Fem: 1104, Mal: 1093, Oth: 99 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
occupation |
0 |
1 |
FALSE |
6 |
Une: 396, Oth: 391, Stu: 383, Tea: 379 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
smoking |
0 |
1 |
FALSE |
2 |
No: 1603, Yes: 693 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
family_history_of_anxiety |
0 |
1 |
FALSE |
2 |
No: 1375, Yes: 921 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
dizziness |
0 |
1 |
FALSE |
2 |
No: 1610, Yes: 686 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
medication |
0 |
1 |
FALSE |
2 |
No: 1819, Yes: 477 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| factor |
recent_major_life_event |
0 |
1 |
FALSE |
2 |
No: 1703, Yes: 593 |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
| numeric |
id |
0 |
1 |
NA |
NA |
NA |
5948.9320557 |
3507.6717992 |
3 |
2859.0 |
5900.5 |
8972.25 |
11997 |
▇▇▇▇▇ |
| numeric |
age |
0 |
1 |
NA |
NA |
NA |
40.7639373 |
13.3314060 |
18 |
29.0 |
41.0 |
52.00 |
64 |
▇▇▇▇▇ |
| numeric |
sleep_hours |
0 |
1 |
NA |
NA |
NA |
6.4772213 |
1.9866656 |
3 |
4.8 |
6.5 |
8.10 |
10 |
▇▇▇▇▆ |
| numeric |
physical_activity_hrs_week |
0 |
1 |
NA |
NA |
NA |
5.0055749 |
2.9230501 |
0 |
2.5 |
4.9 |
7.60 |
10 |
▇▇▇▇▇ |
| numeric |
caffeine_intake_mg_day |
0 |
1 |
NA |
NA |
NA |
450.0357143 |
29.0605938 |
401 |
424.0 |
450.0 |
476.00 |
499 |
▇▇▆▇▇ |
| numeric |
alcohol_consumption_drinks_week |
0 |
1 |
NA |
NA |
NA |
9.4242160 |
5.8163174 |
0 |
4.0 |
9.0 |
14.00 |
19 |
▇▇▇▇▇ |
| numeric |
stress_level_1_10 |
0 |
1 |
NA |
NA |
NA |
5.5479094 |
2.9277971 |
1 |
3.0 |
6.0 |
8.00 |
10 |
▇▇▇▇▇ |
| numeric |
heart_rate_bpm_during_attack |
0 |
1 |
NA |
NA |
NA |
119.3828397 |
34.8738685 |
60 |
88.0 |
119.0 |
150.00 |
179 |
▇▇▇▇▇ |
| numeric |
breathing_rate_breaths_min |
0 |
1 |
NA |
NA |
NA |
25.5953833 |
8.1093053 |
12 |
19.0 |
25.0 |
33.00 |
39 |
▇▆▇▆▇ |
| numeric |
sweating_level_1_5 |
0 |
1 |
NA |
NA |
NA |
2.9947735 |
1.4264750 |
1 |
2.0 |
3.0 |
4.00 |
5 |
▇▇▇▇▇ |
| numeric |
therapy_sessions_per_month |
0 |
1 |
NA |
NA |
NA |
4.6049652 |
2.8410854 |
0 |
2.0 |
5.0 |
7.00 |
9 |
▇▇▇▇▇ |
| numeric |
diet_quality_1_10 |
0 |
1 |
NA |
NA |
NA |
5.5587979 |
2.8532794 |
1 |
3.0 |
6.0 |
8.00 |
10 |
▇▇▇▇▇ |
| numeric |
severity_of_anxiety_attack_1_10 |
0 |
1 |
NA |
NA |
NA |
5.5418118 |
2.8288882 |
1 |
3.0 |
6.0 |
8.00 |
10 |
▇▇▇▇▇ |
| numeric |
high_stress |
0 |
1 |
NA |
NA |
NA |
0.3127178 |
0.4637014 |
0 |
0.0 |
0.0 |
1.00 |
1 |
▇▁▁▁▃ |
| numeric |
high_severity |
0 |
1 |
NA |
NA |
NA |
0.2983449 |
0.4576314 |
0 |
0.0 |
0.0 |
1.00 |
1 |
▇▁▁▁▃ |
| numeric |
untreated |
0 |
1 |
NA |
NA |
NA |
0.0074042 |
0.0857471 |
0 |
0.0 |
0.0 |
0.00 |
1 |
▇▁▁▁▁ |
| numeric |
low_sleep |
0 |
1 |
NA |
NA |
NA |
0.5731707 |
0.4947248 |
0 |
0.0 |
1.0 |
1.00 |
1 |
▆▁▁▁▇ |
| numeric |
high_alcohol |
0 |
1 |
NA |
NA |
NA |
0.4102787 |
0.4919914 |
0 |
0.0 |
0.0 |
1.00 |
1 |
▇▁▁▁▆ |
| numeric |
high_caffeine |
0 |
1 |
NA |
NA |
NA |
1.0000000 |
0.0000000 |
1 |
1.0 |
1.0 |
1.00 |
1 |
▁▁▇▁▁ |
# --- Specific Statistics for Key Questions ---
# Create a data frame to store these results
key_stats <- data.frame(
Question = character(),
Statistic = character(),
Value = numeric(),
stringsAsFactors = FALSE
)
# Question 1: Percentage with severe anxiety attacks in high-stress group
q1_result <- anxiety_data_processed %>%
filter(high_stress == 1) %>%
summarize(percent_high_severity = mean(high_severity) * 100)
key_stats <- rbind(key_stats, data.frame(Question = "Q1", Statistic = "Percent High Severity (High Stress)", Value = q1_result$percent_high_severity))
# Question 3: Proportion untreated in high-stress/high-severity group
q3_result <- anxiety_data_processed %>%
filter(high_stress == 1, high_severity == 1) %>%
summarize(proportion_untreated = mean(untreated) * 100)
key_stats <- rbind(key_stats, data.frame(Question = "Q3", Statistic = "Percent Untreated (High Stress/Severity)", Value = q3_result$proportion_untreated))
# Question 8: average heart rate and breathing rate
q8_high_result <- anxiety_data_processed %>%
filter(severity_of_anxiety_attack_1_10 >= 8) %>%
summarise(average_heart_rate = mean(heart_rate_bpm_during_attack),
average_breathing_rate = mean(breathing_rate_breaths_min))
key_stats <- rbind(key_stats, data.frame(Question = "Q8 (High)", Statistic = "Average Heart Rate", Value = q8_high_result$average_heart_rate))
key_stats <- rbind(key_stats, data.frame(Question = "Q8 (High)", Statistic = "Average Breathing Rate", Value = q8_high_result$average_breathing_rate))
q8_low_result <- anxiety_data_processed %>%
filter(severity_of_anxiety_attack_1_10 < 4) %>%
summarise(average_heart_rate = mean(heart_rate_bpm_during_attack),
average_breathing_rate = mean(breathing_rate_breaths_min))
key_stats <- rbind(key_stats, data.frame(Question = "Q8 (Low)", Statistic = "Average Heart Rate", Value = q8_low_result$average_heart_rate))
key_stats <- rbind(key_stats, data.frame(Question = "Q8 (Low)", Statistic = "Average Breathing Rate", Value = q8_low_result$average_breathing_rate))
# Question 9: median reported severity_of_anxiety_attack_1_10 of those in therapy
q9_result <- anxiety_data_processed %>%
filter(therapy_sessions_per_month > 0) %>%
summarise(median_severity_with_therapy = median(severity_of_anxiety_attack_1_10))
key_stats <- rbind(key_stats, data.frame(Question = "Q9", Statistic = "Median Severity (With Therapy)", Value = q9_result$median_severity_with_therapy))
cat("\nKey Statistics Summary:\n")
Key Statistics Summary:
print(kable(key_stats, format = "markdown"))
| Q1 |
Percent High Severity (High Stress) |
30.434783 |
| Q3 |
Percent Untreated (High Stress/Severity) |
6.400742 |
| Q8 (High) |
Average Heart Rate |
119.224404 |
| Q8 (High) |
Average Breathing Rate |
25.548107 |
| Q8 (Low) |
Average Heart Rate |
119.635797 |
| Q8 (Low) |
Average Breathing Rate |
25.474653 |
| Q9 |
Median Severity (With Therapy) |
6.000000 |
if (capture_all_output) {
print(Sys.time())
sink()
}
[1] "2025-02-07 22:21:22 CST"
6.2. Visualizations
# --- Visualizations ---
if (capture_all_output) {
sink(file = output_file_path, append = TRUE, split = TRUE)
cat("\n\n--- Visualizations ---\n\n")
}
--- Visualizations ---
# --- Stress Level vs. Anxiety Severity (Boxplot) ---
plot_stress_severity <- ggplot(anxiety_data_processed, aes(x = factor(high_stress), y = severity_of_anxiety_attack_1_10)) +
geom_boxplot() +
labs(title = "Anxiety Severity by High Stress",
x = "High Stress (0 = No, 1 = Yes)",
y = "Severity of Anxiety Attack (1-10)")
# Keep print statement for console output
print(plot_stress_severity)

# --- Untreated vs. Treated (Boxplot) ---
plot_untreated_severity <- ggplot(anxiety_data_processed, aes(x = factor(untreated), y = severity_of_anxiety_attack_1_10)) +
geom_boxplot() +
labs(title = "Anxiety Severity by Untreated Status",
x = "Untreated (0 = No, 1 = Yes)",
y = "Severity of Anxiety Attack (1-10)")
print(plot_untreated_severity)

# --- Lifestyle Factors vs. Anxiety Severity (Scatterplots) ---
plot_lifestyle_vs_severity <- function(data, lifestyle_var) {
plot <- ggplot(data, aes(x = .data[[lifestyle_var]], y = severity_of_anxiety_attack_1_10)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm", se = FALSE) +
labs(title = paste("Anxiety Severity vs.", lifestyle_var),
x = lifestyle_var,
y = "Severity of Anxiety Attack (1-10)")
if (capture_all_output) { # Only print if capture_all_output is TRUE
print(plot)
}
return(plot)
}
lifestyle_vars <- c("sleep_hours", "physical_activity_hrs_week", "caffeine_intake_mg_day", "alcohol_consumption_drinks_week", "diet_quality_1_10")
lifestyle_plots <- lapply(lifestyle_vars, function(var) {
plot_lifestyle_vs_severity(anxiety_data_processed, var)
})





# --- Age Groups vs. Anxiety Severity (within high-stress/high-severity) ---
anxiety_data_processed <- anxiety_data_processed %>%
mutate(age_group = cut(age, breaks = c(18, 30, 45, 65), labels = c("18-29", "30-44", "45-64"), include.lowest = TRUE))
plot_age_severity <- ggplot(anxiety_data_processed %>% filter(high_stress == 1, high_severity == 1), aes(x = age_group, y = severity_of_anxiety_attack_1_10)) +
geom_boxplot() +
labs(title = "Anxiety Severity by Age Group (High Stress/Severity)",
x = "Age Group",
y = "Severity of Anxiety Attack (1-10)")
print(plot_age_severity)

# --- Histograms of Key Metrics, Faceted by high_stress and high_severity ---
# Heart Rate
plot_hr_facet <- ggplot(anxiety_data_processed, aes(x = heart_rate_bpm_during_attack)) +
geom_histogram(bins = 30) +
facet_grid(high_stress ~ high_severity) +
labs(title = "Heart Rate Distribution by Stress and Severity",
x = "Heart Rate (bpm)",
y = "Count")
print(plot_hr_facet)

# Breathing Rate
plot_br_facet <- ggplot(anxiety_data_processed, aes(x = breathing_rate_breaths_min)) +
geom_histogram(bins = 30) +
facet_grid(high_stress ~ high_severity) +
labs(title = "Breathing Rate Distribution by Stress and Severity",
x = "Breathing Rate (breaths/min)",
y = "Count")
print(plot_br_facet)

# --- Stacked Bar Plots for Categorical Variables (Question 6) ---
plot_categorical_comparison <- function(data, var_name) {
plot <- ggplot(data, aes(x = factor(severity_of_anxiety_attack_1_10), fill = .data[[var_name]] )) +
geom_bar(position = "fill") +
labs(title = paste("Proportion of", var_name, "by Anxiety Severity"),
x = "Severity of Anxiety Attack (1-10)",
y = "Proportion",
fill = var_name) +
scale_y_continuous(labels = scales::percent)
if (capture_all_output) { # Only print if capture_all_output is TRUE
print(plot)
}
return(plot)
}
categorical_vars_q6 <- c("smoking", "family_history_of_anxiety", "dizziness", "recent_major_life_event")
categorical_comparison_plots <- lapply(categorical_vars_q6, function(var){
plot_categorical_comparison(anxiety_data_processed, var)
})




# Combine and save plots
all_plots <- c(list(plot_stress_severity, plot_untreated_severity),
lifestyle_plots,
list(plot_age_severity, plot_hr_facet, plot_br_facet),
categorical_comparison_plots)
combine_and_save(all_plots, file.path(plots_folder, "combined_analysis_plots.png"), ncol = 3, type = "other")
Error in `wrap_plots()`:
! Only know how to add <ggplot> and/or <grob> objects
Backtrace:
1. global combine_and_save(...)
2. patchwork::wrap_plots(histograms, ncol = ncol, guides = "collect")
6.3. Correlation Analysis
6.4. Statistical Tests (Optional)
6.5. Modeling (Optional)
6.6 Enhancements
6.7 Analysis Summary and Findings
Add a section to summarize all the findings from the different
analysis and tie it with the questions defined in the Ask phase.
---
title: "Anxiety Support App: Data Preparation and Processing"
author: "Your Name - Data Analyst Candidate"
date: "October 26, 2023"
output:
  html_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
capture_all_output <- TRUE  # Set to FALSE for interactive use

library(tidyverse)
library(skimr)
library(here)
library(janitor)
library(patchwork)

# Define output file path
project_root <- here()
data_folder <- file.path(project_root, "data")
output_folder <- file.path(project_root, "output")
plots_folder <- file.path(output_folder, "plots")

# Create output directories
if (!dir.exists(output_folder)) {
  dir.create(output_folder)
}
if (!dir.exists(plots_folder)) {
  dir.create(plots_folder)
}

output_file_path <- file.path(output_folder, "data_prep_process_log.txt") #Updated
data_file_path <- file.path(data_folder, "anxiety_attack_dataset.csv")
```

## 1. Introduction

This report documents the data preparation and processing steps for the "Anxiety Support App: Marketing Audience Identification" case study.  The primary objective is to identify and characterize potential target audiences for the "Calm Button" application.

## 2. Project Setup and Data Acquisition

### 2.1 Loading Necessary Libraries

(Libraries are loaded in the setup chunk)

### 2.2 Defining Project Paths

(Paths are defined in the setup chunk)

### 2.3 Importing the Dataset

```{r import-data}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Importing Data ---\n\n")
}

anxiety_data_raw <- read_csv(data_file_path)

if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

## 3. Detailed Data Inspection (Prepare Phase)

### 3.1 Import check

```{r import-check}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Import Check ---\n\n")
}

head(anxiety_data_raw)

if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 3.2 Column Name Cleaning

```{r clean-names}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Column Name Cleaning ---\n\n")
}

anxiety_data_clean_names <- janitor::clean_names(anxiety_data_raw)

if (capture_all_output) {
    print(Sys.time())
  sink()
}
```

### 3.3 Data Structure

```{r data-structure}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Data Structure ---\n\n")
}

str(anxiety_data_clean_names)

if (capture_all_output) {
    print(Sys.time())
  sink()
}
```

### 3.4 Data Distribution

```{r data-summary}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Data Summary ---\n\n")
}

summary(anxiety_data_clean_names)

if (capture_all_output) {
    print(Sys.time())
  sink()
}
```

### 3.5 Further Details

```{r data-skim}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Detailed Data Summary (skimr) ---\n\n")
}

skim(anxiety_data_clean_names)

if (capture_all_output) {
    print(Sys.time())
  sink()
}
```

## 4. Detailed Variable Examination (Prepare Phase Visualizations)

```{r functions, include=FALSE}
# --- Function for Categorical Variable Visualization ---
plot_categorical <- function(data, var_name) {
  # Create frequency table
  freq_table <- table(data[[var_name]])

  # Create bar plot
  plot <- ggplot(data, aes(x = .data[[var_name]])) +
    geom_bar() +
    labs(title = paste("Distribution of", var_name),
         x = var_name,
         y = "Count") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

  # Print to console (if capture_all_output is TRUE and sink is active)
  if (capture_all_output) {
    print(paste("Frequency Table for", var_name, ":"))
    print(freq_table)
    print(plot)
  }


  return(list(plot = plot, freq_table = freq_table)) # Return plot and table
}

# --- Function for Numeric Variable Visualization ---
plot_numeric <- function(data, var_name) {
  # Create histogram
  plot_hist <- ggplot(data, aes(x = .data[[var_name]])) +
    geom_histogram(bins = 30) +  # Adjust bins as needed
    labs(title = paste("Distribution of", var_name),
         x = var_name,
         y = "Count")

  # Create boxplot
  plot_box <- ggplot(data, aes(y = .data[[var_name]])) +
    geom_boxplot() +
    labs(title = paste("Boxplot of", var_name),
         y = var_name)

  # Print to console
  if (capture_all_output) {
    print(paste("Histogram for", var_name))
    print(plot_hist)
    print(paste("Boxplot for", var_name))
    print(plot_box)
  }

  return(list(histogram = plot_hist, boxplot = plot_box))
}

# --- Function to Combine and Save Plots ---
combine_and_save <- function(plot_list, filename, ncol = 2, type = "categorical") {
  if (type == "categorical") {
    combined_plot <- wrap_plots(plot_list, ncol = ncol, guides = "collect")

  } else {
    # Extract histograms and boxplots
    histograms <- lapply(plot_list, function(x) x$histogram)
    boxplots <- lapply(plot_list, function(x) x$boxplot)

    # Combine histograms and boxplots separately
    combined_histograms <- wrap_plots(histograms, ncol = ncol, guides = "collect")
    combined_boxplots <- wrap_plots(boxplots, ncol = ncol, guides = "collect")

    # Combine both sets of plots (histograms above boxplots)
    combined_plot <- combined_histograms / combined_boxplots # Vertical arrangement
  }

  ggsave(filename, combined_plot, width = 12, height = 8 * (length(plot_list) / 2), dpi = 300) #Adjust height
}

```

### 4.1 Categorical Variable Analysis

```{r categorical-plots}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Categorical Variable Plots ---\n\n")
}

categorical_vars <- c("gender", "occupation", "smoking", "family_history_of_anxiety",
                     "dizziness", "medication", "recent_major_life_event")

# Generate plots and tables, storing them in a list
categorical_results <- lapply(categorical_vars, function(var) {
  plot_categorical(anxiety_data_clean_names, var)
})

# Extract just the plots for combining
categorical_plots <- lapply(categorical_results, function(x) x$plot)

# Combine and save
combine_and_save(categorical_plots, file.path(plots_folder, "combined_categorical_plots.png"), type = "categorical")


if (capture_all_output) {
    print(Sys.time())
  sink()
}
```

### 4.2 Numeric Variable Analysis

```{r numeric-plots}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Numeric Variable Plots ---\n\n")
}

numeric_vars <- c("age", "sleep_hours", "physical_activity_hrs_week",
                 "caffeine_intake_mg_day", "alcohol_consumption_drinks_week",
                 "stress_level_1_10", "heart_rate_bpm_during_attack",
                 "breathing_rate_breaths_min", "sweating_level_1_5",
                 "therapy_sessions_per_month", "diet_quality_1_10",
                 "severity_of_anxiety_attack_1_10")

# Generate plots, storing them in a list
numeric_results <- lapply(numeric_vars, function(var) {
  plot_numeric(anxiety_data_clean_names, var)
})

# Combine and save numeric plots (histograms and boxplots)
combine_and_save(numeric_results, file.path(plots_folder, "combined_numeric_plots.png"), type = "numeric")


if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 4.3 Data Type Conversion Plan

The following variables will be converted to factors in the Process phase:

*   **gender:** Categorical variable.
*   **occupation:** Categorical variable.
*   **smoking:** Categorical (Yes/No).
*   **family_history_of_anxiety:** Categorical (Yes/No).
*   **dizziness:** Categorical (Yes/No).
*   **medication:** Categorical (Yes/No).
*   **recent_major_life_event:** Categorical (Yes/No).

### 4.4 Duplicate Check

```{r duplicate-check}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Duplicate Check ---\n\n")
}

duplicates<- anxiety_data_clean_names %>%
  duplicated() %>%
  sum()
print("Number of Duplicate Rows:")
print(duplicates)

if (capture_all_output) {
    print(Sys.time())
  sink()
}
```

### 4.5 Explicit Missing Value Check

```{r missing-value-check}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Explicit Missing Value Check ---\n\n")
}

missing_values <- colSums(is.na(anxiety_data_clean_names))
print("Missing Values per Column:")
print(missing_values)

missing_percentages <- colMeans(is.na(anxiety_data_clean_names)) * 100
print("Percentage of Missing Values per Column:")
print(missing_percentages)


if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

## 5. Data Processing (Process Phase)

This section details the data cleaning and transformation steps, addressing the issues and plans identified in the Prepare phase.

### 5.1 Data Type Conversion

```{r data-type-conversion}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Data Type Conversion ---\n\n")
}

# Create a copy for processing
anxiety_data_processed <- anxiety_data_clean_names

# Convert character variables to factors
categorical_vars <- c("gender", "occupation", "smoking", "family_history_of_anxiety",
                     "dizziness", "medication", "recent_major_life_event")

anxiety_data_processed <- anxiety_data_processed %>%
  mutate(across(all_of(categorical_vars), as.factor))

# Verify conversion
str(anxiety_data_processed)

if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 5.2 Outlier Investigation and Handling

```{r outlier-handling}
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Outlier Investigation and Handling ---\n\n")
}

# --- sleep_hours ---
# Investigate values < 4
low_sleep <- anxiety_data_processed %>% filter(sleep_hours < 4)
print("Observations with sleep_hours < 4:")
print(low_sleep)
# Decision: Keep.  While low, these values are plausible.

# --- physical_activity_hrs_week ---
# Investigate values > 9
high_activity <- anxiety_data_processed %>% filter(physical_activity_hrs_week > 9)
print("Observations with physical_activity_hrs_week > 9:")
print(high_activity)
# Decision: Keep. These are high but plausible values.

# --- caffeine_intake_mg_day ---
# Investigate values > 400
high_caffeine <- anxiety_data_processed %>% filter(caffeine_intake_mg_day > 400)
print("Observations with caffeine_intake_mg_day > 400:")
print(high_caffeine)
# Decision: Keep. These are high, but plausible, values.

# --- alcohol_consumption_drinks_week ---
# Investigate values > 14
high_alcohol <- anxiety_data_processed %>% filter(alcohol_consumption_drinks_week > 14)
print("Observations with alcohol_consumption_drinks_week > 14:")
print(high_alcohol)
# Decision: Keep. While above recommended limits, they are plausible.

# --- heart_rate_bpm_during_attack ---
# Investigate values < 70 and > 160
low_hr <- anxiety_data_processed %>% filter(heart_rate_bpm_during_attack < 70)
print("Observations with heart_rate_bpm_during_attack < 70:")
print(low_hr)

high_hr <- anxiety_data_processed %>% filter(heart_rate_bpm_during_attack > 160)
print("Observations with heart_rate_bpm_during_attack > 160:")
print(high_hr)
# Decision: Keep. After reviewing the context, values are kept.

# --- breathing_rate_breaths_min ---
# Investigate values < 15 and > 35
low_br <- anxiety_data_processed %>% filter(breathing_rate_breaths_min < 15)
print("Observations with breathing_rate_breaths_min < 15:")
print(low_br)
high_br <- anxiety_data_processed %>% filter(breathing_rate_breaths_min > 35)
print("Observations with breathing_rate_breaths_min > 35:")
print(high_br)
# Decision: Keep. After reviewing the context, values are kept.

if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 5.3. Variable Creation

```{r variable-creation}
if (capture_all_output) {
    sink(file = output_file_path, append = TRUE, split = TRUE)
    cat("\n\n--- Variable Creation ---\n\n")
}

# --- High Stress Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
  mutate(high_stress = ifelse(stress_level_1_10 >= 8, 1, 0))

# --- High Severity Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
  mutate(high_severity = ifelse(severity_of_anxiety_attack_1_10 >= 8, 1, 0))

# --- Untreated Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
  mutate(untreated = ifelse(high_stress == 1 & high_severity == 1 & therapy_sessions_per_month == 0 & medication == "No", 1, 0))

# --- Low Sleep Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
      mutate(low_sleep = ifelse(sleep_hours < 7, 1, 0))


# --- High Alcohol Consumption Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
      mutate(high_alcohol = ifelse( (gender == "Female" & alcohol_consumption_drinks_week >= 8) |
                                     (gender == "Male"   & alcohol_consumption_drinks_week >= 15) |
                                     (gender == "Other" & alcohol_consumption_drinks_week >= 15)
                                   , 1, 0))

# --- High Caffeine Consumption Indicator ---
anxiety_data_processed <- anxiety_data_processed %>%
    mutate(high_caffeine = ifelse(caffeine_intake_mg_day > 400, 1, 0))

#Verify
str(anxiety_data_processed)

if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 5.4. Verification

```{r verification}
# --- Verification ---
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Verification ---\n\n")
}

# Check for NA's again in the new variables
missing_values_processed <- colSums(is.na(anxiety_data_processed))
print("Missing Values per Column After Processing:")
print(missing_values_processed)

# Check for Duplicates again
duplicates_processed <- anxiety_data_processed %>%
  duplicated() %>%
  sum()
print("Number of Duplicate Rows After Processing:")
print(duplicates_processed)

if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

## 6. Data Analysis (Analyze Phase)

### 6.1. Descriptive Statistics (Targeted Groups)

```{r descriptive-stats}
# --- Descriptive Statistics (Targeted Groups) ---
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Descriptive Statistics (Targeted Groups) ---\n\n")
}

library(knitr) # Make sure knitr is loaded

# --- Overall Descriptive Statistics ---
cat("\nOverall Descriptive Statistics:\n")
print(kable(skim(anxiety_data_processed), format = "markdown"))


# --- High Stress Group (stress_level_1_10 >= 8) ---
cat("\nHigh Stress Group (stress_level_1_10 >= 8):\n")
high_stress_skim <- anxiety_data_processed %>%
  filter(high_stress == 1) %>%
  skim()
print(kable(high_stress_skim, format = "markdown"))

# --- High Severity Group (severity_of_anxiety_attack_1_10 >= 8) ---
cat("\nHigh Severity Group (severity_of_anxiety_attack_1_10 >= 8):\n")
high_severity_skim <- anxiety_data_processed %>%
  filter(high_severity == 1) %>%
  skim()
print(kable(high_severity_skim, format = "markdown"))


# --- Untreated Group (high_stress, high_severity, no therapy/medication) ---
cat("\nUntreated Group (High Stress, High Severity, No Therapy/Medication):\n")
untreated_skim <- anxiety_data_processed %>%
  filter(untreated == 1) %>%
  skim()
print(kable(untreated_skim, format = "markdown"))


# --- Low Sleep Group (sleep_hours < 7) ---
cat("\nLow Sleep Group (sleep_hours < 7):\n")
low_sleep_skim <- anxiety_data_processed %>%
  filter(low_sleep == 1) %>%
  skim()
print(kable(low_sleep_skim, format = "markdown"))

# --- High Alcohol Group ---
cat("\nHigh Alcohol Group:\n")
high_alcohol_skim <- anxiety_data_processed %>%
  filter(high_alcohol == 1) %>%
  skim()
print(kable(high_alcohol_skim, format = "markdown"))


# --- High Caffeine Group ---
cat("\nHigh Caffeine Group:\n")
high_caffeine_skim <- anxiety_data_processed %>%
  filter(high_caffeine == 1) %>%
  skim()
print(kable(high_caffeine_skim, format = "markdown"))

# --- Specific Statistics for Key Questions ---
# Create a data frame to store these results
key_stats <- data.frame(
  Question = character(),
  Statistic = character(),
  Value = numeric(),
  stringsAsFactors = FALSE
)

# Question 1: Percentage with severe anxiety attacks in high-stress group

q1_result <- anxiety_data_processed %>%
  filter(high_stress == 1) %>%
  summarize(percent_high_severity = mean(high_severity) * 100)
key_stats <- rbind(key_stats, data.frame(Question = "Q1", Statistic = "Percent High Severity (High Stress)", Value = q1_result$percent_high_severity))


# Question 3: Proportion untreated in high-stress/high-severity group

q3_result <- anxiety_data_processed %>%
  filter(high_stress == 1, high_severity == 1) %>%
  summarize(proportion_untreated = mean(untreated) * 100)
key_stats <- rbind(key_stats, data.frame(Question = "Q3", Statistic = "Percent Untreated (High Stress/Severity)", Value = q3_result$proportion_untreated))


# Question 8: average heart rate and breathing rate
q8_high_result <- anxiety_data_processed %>%
    filter(severity_of_anxiety_attack_1_10 >= 8) %>%
    summarise(average_heart_rate = mean(heart_rate_bpm_during_attack),
              average_breathing_rate = mean(breathing_rate_breaths_min))
key_stats <- rbind(key_stats, data.frame(Question = "Q8 (High)", Statistic = "Average Heart Rate", Value = q8_high_result$average_heart_rate))
key_stats <- rbind(key_stats, data.frame(Question = "Q8 (High)", Statistic = "Average Breathing Rate", Value = q8_high_result$average_breathing_rate))


q8_low_result <- anxiety_data_processed %>%
    filter(severity_of_anxiety_attack_1_10 < 4) %>%
    summarise(average_heart_rate = mean(heart_rate_bpm_during_attack),
              average_breathing_rate = mean(breathing_rate_breaths_min))
key_stats <- rbind(key_stats, data.frame(Question = "Q8 (Low)", Statistic = "Average Heart Rate", Value = q8_low_result$average_heart_rate))
key_stats <- rbind(key_stats, data.frame(Question = "Q8 (Low)", Statistic = "Average Breathing Rate", Value = q8_low_result$average_breathing_rate))

# Question 9: median reported severity_of_anxiety_attack_1_10 of those in therapy
q9_result <- anxiety_data_processed %>%
    filter(therapy_sessions_per_month > 0) %>%
    summarise(median_severity_with_therapy = median(severity_of_anxiety_attack_1_10))
key_stats <- rbind(key_stats, data.frame(Question = "Q9", Statistic = "Median Severity (With Therapy)", Value = q9_result$median_severity_with_therapy))


cat("\nKey Statistics Summary:\n")
print(kable(key_stats, format = "markdown"))


if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 6.2. Visualizations

```{r visualizations}
# --- Visualizations ---
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Visualizations ---\n\n")
}

# --- Stress Level vs. Anxiety Severity (Boxplot) ---
plot_stress_severity <- ggplot(anxiety_data_processed, aes(x = factor(high_stress), y = severity_of_anxiety_attack_1_10)) +
  geom_boxplot() +
  labs(title = "Anxiety Severity by High Stress",
       x = "High Stress (0 = No, 1 = Yes)",
       y = "Severity of Anxiety Attack (1-10)")
# Keep print statement for console output
print(plot_stress_severity)

# --- Untreated vs. Treated (Boxplot) ---
plot_untreated_severity <- ggplot(anxiety_data_processed, aes(x = factor(untreated), y = severity_of_anxiety_attack_1_10)) +
    geom_boxplot() +
    labs(title = "Anxiety Severity by Untreated Status",
         x = "Untreated (0 = No, 1 = Yes)",
         y = "Severity of Anxiety Attack (1-10)")
print(plot_untreated_severity)

# --- Lifestyle Factors vs. Anxiety Severity (Scatterplots) ---

plot_lifestyle_vs_severity <- function(data, lifestyle_var) {
  plot <- ggplot(data, aes(x = .data[[lifestyle_var]], y = severity_of_anxiety_attack_1_10)) +
    geom_point(alpha = 0.3) +
    geom_smooth(method = "lm", se = FALSE) +
    labs(title = paste("Anxiety Severity vs.", lifestyle_var),
         x = lifestyle_var,
         y = "Severity of Anxiety Attack (1-10)")
  if (capture_all_output) { # Only print if capture_all_output is TRUE
        print(plot)
    }
  return(plot)
}

lifestyle_vars <- c("sleep_hours", "physical_activity_hrs_week", "caffeine_intake_mg_day", "alcohol_consumption_drinks_week", "diet_quality_1_10")

lifestyle_plots <- lapply(lifestyle_vars, function(var) {
  plot_lifestyle_vs_severity(anxiety_data_processed, var)
})


# --- Age Groups vs. Anxiety Severity (within high-stress/high-severity) ---
anxiety_data_processed <- anxiety_data_processed %>%
  mutate(age_group = cut(age, breaks = c(18, 30, 45, 65), labels = c("18-29", "30-44", "45-64"), include.lowest = TRUE))

plot_age_severity <- ggplot(anxiety_data_processed %>% filter(high_stress == 1, high_severity == 1), aes(x = age_group, y = severity_of_anxiety_attack_1_10)) +
  geom_boxplot() +
  labs(title = "Anxiety Severity by Age Group (High Stress/Severity)",
       x = "Age Group",
       y = "Severity of Anxiety Attack (1-10)")
print(plot_age_severity)
# --- Histograms of Key Metrics, Faceted by high_stress and high_severity ---

# Heart Rate
plot_hr_facet <- ggplot(anxiety_data_processed, aes(x = heart_rate_bpm_during_attack)) +
  geom_histogram(bins = 30) +
  facet_grid(high_stress ~ high_severity) +
  labs(title = "Heart Rate Distribution by Stress and Severity",
       x = "Heart Rate (bpm)",
       y = "Count")
print(plot_hr_facet)

# Breathing Rate
plot_br_facet <- ggplot(anxiety_data_processed, aes(x = breathing_rate_breaths_min)) +
  geom_histogram(bins = 30) +
  facet_grid(high_stress ~ high_severity) +
  labs(title = "Breathing Rate Distribution by Stress and Severity",
       x = "Breathing Rate (breaths/min)",
       y = "Count")
print(plot_br_facet)

# --- Stacked Bar Plots for Categorical Variables (Question 6) ---
plot_categorical_comparison <- function(data, var_name) {
    plot <- ggplot(data, aes(x = factor(severity_of_anxiety_attack_1_10), fill = .data[[var_name]] )) +
        geom_bar(position = "fill") +
        labs(title = paste("Proportion of", var_name, "by Anxiety Severity"),
        x = "Severity of Anxiety Attack (1-10)",
        y = "Proportion",
        fill = var_name) +
        scale_y_continuous(labels = scales::percent)
    if (capture_all_output) { # Only print if capture_all_output is TRUE
        print(plot)
    }
    return(plot)
}

categorical_vars_q6 <- c("smoking", "family_history_of_anxiety", "dizziness", "recent_major_life_event")

categorical_comparison_plots <- lapply(categorical_vars_q6, function(var){
    plot_categorical_comparison(anxiety_data_processed, var)
})

# Combine and save plots
all_plots <- c(list(plot_stress_severity, plot_untreated_severity),
               lifestyle_plots,
               list(plot_age_severity, plot_hr_facet, plot_br_facet),
               categorical_comparison_plots)

combine_and_save(all_plots, file.path(plots_folder, "combined_analysis_plots.png"), ncol = 3, type = "other")


if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 6.3. Correlation Analysis

```{r correlation-analysis}
# --- Correlation Analysis ---
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Correlation Analysis ---\n\n")
}

# Calculate correlations
correlations <- anxiety_data_processed %>%
  select(all_of(lifestyle_vars), severity_of_anxiety_attack_1_10) %>%
  cor(use = "pairwise.complete.obs")  # Handle potential missing values (though we have none)

print("Correlation Matrix:")
print(kable(correlations, format = "markdown"))

# Perform correlation tests (for statistical significance)
correlation_tests <- lapply(lifestyle_vars, function(var) {
  cor.test(anxiety_data_processed[[var]], anxiety_data_processed$severity_of_anxiety_attack_1_10)
})
names(correlation_tests) <- lifestyle_vars # Name the list elements

print("Correlation Tests:")
# Using capture.output to format the correlation test results
for (var in names(correlation_tests)) {
    cat(paste("\n*** Correlation Test for:", var, "***\n"))
    cat(capture.output(print(correlation_tests[[var]])), sep = "\n")
}


if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 6.4. Statistical Tests (Optional)

```{r statistical-tests}
# --- Statistical Tests ---

if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Statistical Tests ---\n\n")
}

# --- T-test: Heart Rate by High Severity ---
# Question 8
t_test_hr <- t.test(heart_rate_bpm_during_attack ~ high_severity, data = anxiety_data_processed)
print("T-test: Heart Rate by High Severity")
print(t_test_hr)


# --- Chi-squared Test: Smoking by High Severity ---
# Question 6
chisq_test_smoking <- chisq.test(anxiety_data_processed$smoking, anxiety_data_processed$high_severity)
print("Chi-squared Test: Smoking by High Severity")
print(chisq_test_smoking)

if (capture_all_output) {
    print(Sys.time())
    sink()
}
```

### 6.5. Modeling (Optional)

```{r modeling}
# --- Logistic Regression Model (Optional) ---

if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Logistic Regression Model ---\n\n")
}

# Build the model (predicting high_severity)
model <- glm(high_severity ~ age + gender + sleep_hours + physical_activity_hrs_week +
               caffeine_intake_mg_day + alcohol_consumption_drinks_week +
               smoking + family_history_of_anxiety + stress_level_1_10 +
               dizziness + medication + therapy_sessions_per_month +
               recent_major_life_event + diet_quality_1_10 + low_sleep + high_alcohol + high_caffeine,
             data = anxiety_data_processed, family = "binomial")

# Summarize the model
summary(model)

if (capture_all_output) {
    print(Sys.time())
    sink()
}
```

### 6.6 Enhancements

```{r analyze-enhancements}
# --- Enhancements to Analyze Phase ---

if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Analyze Phase Enhancements ---\n\n")
}

# --- Question 2: Compare Lifestyle Factors to Overall Averages ---
cat("\nQuestion 2: Lifestyle Factor Comparisons (High Stress/Severity vs. Overall):\n")

# Calculate overall averages
overall_means <- anxiety_data_processed %>%
  summarize(across(all_of(lifestyle_vars), ~ mean(.x, na.rm = TRUE)))

# Calculate averages for high-stress/high-severity group
high_stress_severity_means <- anxiety_data_processed %>%
  filter(high_stress == 1, high_severity == 1) %>%
  summarize(across(all_of(lifestyle_vars), ~ mean(.x, na.rm = TRUE)))

# Combine and print.  Use kable for nice formatting.
comparison_table <- bind_rows(
  "Overall" = overall_means,
  "High Stress/Severity" = high_stress_severity_means,
  .id = "Group"
)
print(kable(comparison_table, format = "markdown"))


# --- Question 4: Age Group Analysis within High Stress/Severity ---
cat("\nQuestion 4: Age Group Analysis within High Stress/Severity:\n")

# Lifestyle factors by age group
lifestyle_by_age <- anxiety_data_processed %>%
  filter(high_stress == 1, high_severity == 1) %>%
  group_by(age_group) %>%
  summarize(across(all_of(lifestyle_vars), ~ mean(.x, na.rm = TRUE)))
print(kable(lifestyle_by_age, format = "markdown"))


# Treatment usage by age group
treatment_by_age <- anxiety_data_processed %>%
  filter(high_stress == 1, high_severity == 1) %>%
  group_by(age_group) %>%
  summarize(percent_therapy = mean(therapy_sessions_per_month > 0) * 100,
            percent_medication = mean(medication == "Yes") * 100)
print(kable(treatment_by_age, format = "markdown"))


# --- Question 6: High vs. Low Severity Comparisons ---
cat("\nQuestion 6: High vs. Low Severity Comparisons:\n")

# Calculate proportions for high severity
high_severity_props <- anxiety_data_processed %>%
  filter(high_severity == 1) %>%
  summarize(across(all_of(categorical_vars_q6), ~ mean(.x == "Yes") * 100))

# Calculate proportions for low severity
low_severity_props <- anxiety_data_processed %>%
  filter(severity_of_anxiety_attack_1_10 < 4) %>%
  summarize(across(all_of(categorical_vars_q6), ~ mean(.x == "Yes") * 100))

# Combine and print
comparison_table_q6 <- bind_rows(
  "High Severity" = high_severity_props,
  "Low Severity" = low_severity_props,
  .id = "Group"
)
print(kable(comparison_table_q6, format = "markdown"))

# Chi-squared tests for each variable, capturing output
for (var in categorical_vars_q6) {
    cat(paste("\nChi-squared test for", var, ":\n"))
    tbl <- table(anxiety_data_processed[[var]], anxiety_data_processed$high_severity)
    print(capture.output(chisq.test(tbl)))
}

if (capture_all_output) {
  print(Sys.time())
  sink()
}
```

### 6.7 Analysis Summary and Findings

Add a section to summarize all the findings from the different analysis and tie it with the questions defined in the Ask phase.

```{r analysis-summary}
# --- Analysis Summary and Findings ---
if (capture_all_output) {
  sink(file = output_file_path, append = TRUE, split = TRUE)
  cat("\n\n--- Analysis Summary and Findings ---\n\n")
}
#To be added in the next step
if (capture_all_output) {
    print(Sys.time())
    sink()
}
```

```{r close-sink, include=FALSE, eval=capture_all_output}
# Ensure sink is closed even if there's an error in a previous chunk
if (sink.number() > 0) {
    print(Sys.time())
  sink()
}
```


